# Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network

## TL;DR

This paper introduces a neural network-based tool for forecasting marine heatwaves in the Indian Ocean up to 10 weeks in advance.

## Contribution

A U-Net-based neural network model for subseasonal marine heatwave forecasting in the Northern Indian Ocean and Arabian Sea.

## Key findings

- The U-Net-based model showed significant predictive skill up to 10 weeks ahead.
- It outperformed persistence and climatology benchmarks in the tropical warm pool region.

## Abstract

Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.